Multilevel Modeling and its Application in Counseling Psychology Research

1999 ◽  
Vol 27 (4) ◽  
pp. 528-551 ◽  
Author(s):  
Steven P. Reise ◽  
Naihua Duan
Keyword(s):  
Multilevel Modeling ◽  
Counseling Psychology ◽  
Predictor Variables ◽  
Outcome Variable ◽  
Hierarchical Data ◽  
Psychology Research ◽  
Data Set ◽  
Data Analyses ◽  
Different Levels ◽  
Group Effects

Multilevel modeling (MLM) should be used when a researcher has collected hierarchical data. For example, when a researcher investigates an outcome variable (e.g., depression) with several clients drawn from different clinicians, the data set has a hierarchical structure. Herein, we describe the use of MLM in counseling research. The goals include the following: (a) to specify research contexts where MLM may be applied, (b) to describe how to conduct data analyses using MLM, and (c) to highlight key statistical and design issues encountered when analyzing hierarchical data. We also highlight how MLM can be used (a) to provide valid statistical inference in the presence of hierarchical data structure, (b) to separate the within-group effects from between-group effects for predictor variables, and (c) to study the interactions among predictor variables drawn from different levels (e.g., variables drawn from both clients and their clinicians).

scholarly journals Hospital acquired pressure injury prediction in surgical critical care patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jenny Alderden ◽  
Kathryn P. Drake ◽  
Andrew Wilson ◽  
Jonathan Dimas ◽  
Mollie R. Cummins ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Performance ◽  
Routine Care ◽  
Predictor Variables ◽  
Classification Algorithms ◽  
Data Set ◽  
Icu Patients ◽  
Pressure Injury ◽  
Surgical Icu ◽  
Hospital Acquired

Abstract Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Using Social Class in Counseling Psychology Research.

2004 ◽  
Vol 51 (1) ◽  
pp. 3-18 ◽  
Author(s):  
William Ming Liu ◽  
Saba Rasheed Ali ◽  
Geoff Soleck ◽  
Joshua Hopps ◽  
Kwesi dunston ◽  
...  
Keyword(s):  
Social Class ◽  
Counseling Psychology ◽  
Psychology Research

scholarly journals Technical note: Multiple wavelet coherence for untangling scale-specific and localized multivariate relationships in geosciences

2016 ◽  
Vol 20 (8) ◽  
pp. 3183-3191 ◽  
Author(s):  
Wei Hu ◽  
Bing Cheng Si
Keyword(s):  
Multiple Scales ◽  
Free Water ◽  
Wavelet Coherence ◽  
Predictor Variables ◽  
Water Evaporation ◽  
Multivariate Methods ◽  
Spectral Coherence ◽  
Data Set ◽  
Multivariate Relationships ◽  
Multiple Wavelet Coherence

Abstract. The scale-specific and localized bivariate relationships in geosciences can be revealed using bivariate wavelet coherence. The objective of this study was to develop a multiple wavelet coherence method for examining scale-specific and localized multivariate relationships. Stationary and non-stationary artificial data sets, generated with the response variable as the summation of five predictor variables (cosine waves) with different scales, were used to test the new method. Comparisons were also conducted using existing multivariate methods, including multiple spectral coherence and multivariate empirical mode decomposition (MEMD). Results show that multiple spectral coherence is unable to identify localized multivariate relationships, and underestimates the scale-specific multivariate relationships for non-stationary processes. The MEMD method was able to separate all variables into components at the same set of scales, revealing scale-specific relationships when combined with multiple correlation coefficients, but has the same weakness as multiple spectral coherence. However, multiple wavelet coherences are able to identify scale-specific and localized multivariate relationships, as they are close to 1 at multiple scales and locations corresponding to those of predictor variables. Therefore, multiple wavelet coherence outperforms other common multivariate methods. Multiple wavelet coherence was applied to a real data set and revealed the optimal combination of factors for explaining temporal variation of free water evaporation at the Changwu site in China at multiple scale-location domains. Matlab codes for multiple wavelet coherence were developed and are provided in the Supplement.

An Empirical Study of Factors Influencing Consumer Attitudes towards SMS Advertising

2014 ◽  
Vol 4 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Pradeep Dharmadasa ◽  
Thilini Alahakoon
Keyword(s):  
Empirical Study ◽  
Structural Equation ◽  
Business Management ◽  
Consumer Attitudes ◽  
Predictor Variables ◽  
Outcome Variable ◽  
Factors Influencing ◽  
Central Route ◽  
Customer Attitudes ◽  
The University

This article examines factors influencing consumer attitudes towards SMS advertising. The study's research framework was conceptualized using five predictor variables – informativeness, irritation, privacy, credibility, and incentives – and an outcome variable of consumer attitudes towards SMS advertising. The informativeness, irritation, and privacy was labelled as central route constructs and credibility and incentives were labelled as peripheral route constructs. Survey data collected from 251 mobile users selected from a cohort of undergraduates in business management from the University of Colombo, Sri Lanka, were analyzed using the Structural Equation Method (SEM). Results suggest that the informativeness and incentive variables are positively associated with customer attitudes towards SMS advertising, whereas irritation and privacy are found to be negatively associated with consumer attitudes towards SMS advertising. Surprisingly, credibility was found to be an insignificant factor predicting consumer attitudes towards SMS advertising. Several implications for consumer attitudes towards SMS advertising are discussed.

Sexism and Gender Inequality Across 57 Societies

2011 ◽  
Vol 22 (11) ◽  
pp. 1413-1418 ◽  
Author(s):  
Mark J. Brandt
Keyword(s):  
Longitudinal Data ◽  
Multilevel Modeling ◽  
Gender Inequality ◽  
Past Research ◽  
Status Quo ◽  
Data Set ◽  
Representative Data ◽  
Gender Hierarchy ◽  
And Gender

Theory predicts that individuals’ sexism serves to exacerbate inequality in their society’s gender hierarchy. Past research, however, has provided only correlational evidence to support this hypothesis. In this study, I analyzed a large longitudinal data set that included representative data from 57 societies. Multilevel modeling showed that sexism directly predicted increases in gender inequality. This study provides the first evidence that sexist ideologies can create gender inequality within societies, and this finding suggests that sexism not only legitimizes the societal status quo, but also actively enhances the severity of the gender hierarchy. Three potential mechanisms for this effect are discussed briefly.

Replication of Data Analyses

2017 ◽  
Author(s):  
Emery R. Boose ◽  
Barbara S. Lerner
Keyword(s):  
Original Work ◽  
Statistical Tests ◽  
Scientific Paper ◽  
Scientific Data ◽  
Data Sources ◽  
Narrative Form ◽  
Data Provenance ◽  
General Description ◽  
Data Set ◽  
Data Analyses

The metadata that describe how scientific data are created and analyzed are typically limited to a general description of data sources, software used, and statistical tests applied and are presented in narrative form in the methods section of a scientific paper or a data set description. Recognizing that such narratives are usually inadequate to support reproduction of the analysis of the original work, a growing number of journals now require that authors also publish their data. However, finer-scale metadata that describe exactly how individual items of data were created and transformed and the processes by which this was done are rarely provided, even though such metadata have great potential to improve data set reliability. This chapter focuses on the detailed process metadata, called “data provenance,” required to ensure reproducibility of analyses and reliable re-use of the data.

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